Overview

Dataset statistics

Number of variables11
Number of observations360
Missing cells270
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.1 KiB
Average record size in memory88.4 B

Variable types

NUM10
CAT1

Warnings

production_layers_compounds_ipu_thsdtonnes is highly correlated with yearHigh correlation
year is highly correlated with production_layers_compounds_ipu_thsdtonnesHigh correlation
production_layers_compounds_rtl_thsdtonnes is highly correlated with production_broiler_chicken_compounds_rtl_thsdtonnesHigh correlation
production_broiler_chicken_compounds_rtl_thsdtonnes is highly correlated with production_layers_compounds_rtl_thsdtonnes and 1 other fieldsHigh correlation
production_total_poultry_feed_rtl_thsdtonnes is highly correlated with production_broiler_chicken_compounds_rtl_thsdtonnesHigh correlation
production_total_production_ipu_thsdtonnes is highly correlated with production_broiler_chicken_compounds_ipu_thsdtonnesHigh correlation
production_broiler_chicken_compounds_ipu_thsdtonnes is highly correlated with production_total_production_ipu_thsdtonnesHigh correlation
production_broiler_chicken_compounds_ipu_thsdtonnes has 42 (11.7%) missing values Missing
production_broiler_chicken_compounds_rtl_thsdtonnes has 6 (1.7%) missing values Missing
production_layers_compounds_ipu_thsdtonnes has 42 (11.7%) missing values Missing
production_layers_compounds_rtl_thsdtonnes has 6 (1.7%) missing values Missing
production_total_poultry_feed_rtl_thsdtonnes has 6 (1.7%) missing values Missing
production_total_production_ipu_thsdtonnes has 42 (11.7%) missing values Missing
production_broiler_chicken_compounds_thsdtonnes has 42 (11.7%) missing values Missing
production_layers_compounds_thsdtonnes has 42 (11.7%) missing values Missing
production_total_poultry_feed_thsdtonnes has 42 (11.7%) missing values Missing
month is uniformly distributed Uniform

Reproduction

Analysis started2022-04-08 17:19:55.226397
Analysis finished2022-04-08 17:20:22.176392
Duration26.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:22.358876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993.95
Q11999.75
median2007
Q32014.25
95-th percentile2020.05
Maximum2022
Range30
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.681937835
Coefficient of variation (CV)0.004325828518
Kurtosis-1.194611979
Mean2007
Median Absolute Deviation (MAD)7.5
Skewness0
Sum722520
Variance75.37604457
MonotocityIncreasing
2022-04-08T10:20:22.513500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
2011123.3%
 
2010123.3%
 
2016123.3%
 
1996123.3%
 
2009123.3%
 
2007123.3%
 
2012123.3%
 
1999123.3%
 
1997123.3%
 
2006123.3%
 
Other values (21)24066.7%
 
ValueCountFrequency (%) 
199261.7%
 
1993123.3%
 
1994123.3%
 
1995123.3%
 
1996123.3%
 
ValueCountFrequency (%) 
202261.7%
 
2021123.3%
 
2020123.3%
 
2019123.3%
 
2018123.3%
 

month
Categorical

UNIFORM

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Jun
30 
Nov
30 
Dec
30 
May
30 
Sep
30 
Other values (7)
210 
ValueCountFrequency (%) 
Jun308.3%
 
Nov308.3%
 
Dec308.3%
 
May308.3%
 
Sep308.3%
 
Feb308.3%
 
Mar308.3%
 
Jan308.3%
 
Apr308.3%
 
Oct308.3%
 
Other values (2)6016.7%
 
2022-04-08T10:20:22.732874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-08T10:20:22.881478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

production_broiler_chicken_compounds_ipu_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct259
Distinct (%)81.4%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean158.0727673
Minimum109.8
Maximum215.6
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:23.036830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum109.8
5-th percentile115.785
Q1145.45
median156.6
Q3175.2
95-th percentile198.345
Maximum215.6
Range105.8
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation24.34205939
Coefficient of variation (CV)0.1539927453
Kurtosis-0.3178881038
Mean158.0727673
Median Absolute Deviation (MAD)14.35
Skewness0.03645615906
Sum50267.14
Variance592.5358554
MonotocityNot monotonic
2022-04-08T10:20:23.228647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
156.141.1%
 
156.941.1%
 
152.230.8%
 
158.930.8%
 
180.430.8%
 
160.520.6%
 
146.720.6%
 
161.320.6%
 
18320.6%
 
116.320.6%
 
Other values (249)29180.8%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
109.810.3%
 
109.910.3%
 
11010.3%
 
111.620.6%
 
111.810.3%
 
ValueCountFrequency (%) 
215.610.3%
 
214.610.3%
 
213.910.3%
 
211.810.3%
 
210.610.3%
 

production_broiler_chicken_compounds_rtl_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct272
Distinct (%)76.8%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean106.7920904
Minimum58.9
Maximum229.2
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:23.469003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum58.9
5-th percentile68.53
Q176.35
median88.85
Q3123.9
95-th percentile190.845
Maximum229.2
Range170.3
Interquartile range (IQR)47.55

Descriptive statistics

Standard deviation41.03329049
Coefficient of variation (CV)0.3842352963
Kurtosis0.1597941768
Mean106.7920904
Median Absolute Deviation (MAD)16.15
Skewness1.151824619
Sum37804.4
Variance1683.730929
MonotocityNot monotonic
2022-04-08T10:20:23.748260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
167.741.1%
 
72.741.1%
 
83.841.1%
 
77.341.1%
 
75.430.8%
 
96.330.8%
 
82.430.8%
 
92.630.8%
 
92.530.8%
 
75.630.8%
 
Other values (262)32088.9%
 
(Missing)61.7%
 
ValueCountFrequency (%) 
58.910.3%
 
5910.3%
 
62.210.3%
 
6310.3%
 
63.410.3%
 
ValueCountFrequency (%) 
229.210.3%
 
223.310.3%
 
220.210.3%
 
219.110.3%
 
213.310.3%
 

production_layers_compounds_ipu_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct166
Distinct (%)52.2%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean19.09012579
Minimum8.3
Maximum39
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:23.950778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile11.3
Q114.025
median18.65
Q322.5
95-th percentile29.275
Maximum39
Range30.7
Interquartile range (IQR)8.475

Descriptive statistics

Standard deviation6.081944265
Coefficient of variation (CV)0.3185911048
Kurtosis-0.1108501137
Mean19.09012579
Median Absolute Deviation (MAD)4.35
Skewness0.6105154838
Sum6070.66
Variance36.99004604
MonotocityNot monotonic
2022-04-08T10:20:24.253780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14.361.7%
 
19.461.7%
 
11.851.4%
 
1251.4%
 
14.841.1%
 
1441.1%
 
1341.1%
 
14.541.1%
 
21.341.1%
 
13.141.1%
 
Other values (156)27275.6%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
8.310.3%
 
8.510.3%
 
8.720.6%
 
8.810.3%
 
9.510.3%
 
ValueCountFrequency (%) 
3910.3%
 
3510.3%
 
34.7610.3%
 
34.720.6%
 
34.610.3%
 

production_layers_compounds_rtl_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct238
Distinct (%)67.2%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean76.88672316
Minimum56.5
Maximum115.6
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:24.512089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum56.5
5-th percentile59.4
Q166.625
median75.4
Q386.15
95-th percentile100.75
Maximum115.6
Range59.1
Interquartile range (IQR)19.525

Descriptive statistics

Standard deviation12.95411248
Coefficient of variation (CV)0.1684830871
Kurtosis-0.2033152354
Mean76.88672316
Median Absolute Deviation (MAD)9.5
Skewness0.6167017552
Sum27217.9
Variance167.80903
MonotocityNot monotonic
2022-04-08T10:20:24.896067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
66.841.1%
 
65.641.1%
 
61.641.1%
 
76.141.1%
 
69.641.1%
 
80.430.8%
 
81.230.8%
 
77.530.8%
 
82.130.8%
 
65.230.8%
 
Other values (228)31988.6%
 
(Missing)61.7%
 
ValueCountFrequency (%) 
56.520.6%
 
56.710.3%
 
56.810.3%
 
57.430.8%
 
57.610.3%
 
ValueCountFrequency (%) 
115.610.3%
 
114.310.3%
 
111.710.3%
 
111.310.3%
 
110.810.3%
 

production_total_poultry_feed_rtl_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct312
Distinct (%)88.1%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean263.0929379
Minimum179.1
Maximum442.4
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:25.263625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum179.1
5-th percentile200.165
Q1226.3
median244.95
Q3288.575
95-th percentile375.69
Maximum442.4
Range263.3
Interquartile range (IQR)62.275

Descriptive statistics

Standard deviation54.11016704
Coefficient of variation (CV)0.2056694014
Kurtosis0.44683932
Mean263.0929379
Median Absolute Deviation (MAD)24.2
Skewness1.088983268
Sum93134.9
Variance2927.910177
MonotocityNot monotonic
2022-04-08T10:20:25.626652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
247.830.8%
 
297.530.8%
 
246.630.8%
 
243.330.8%
 
228.630.8%
 
236.620.6%
 
231.920.6%
 
246.720.6%
 
191.120.6%
 
227.420.6%
 
Other values (302)32991.4%
 
(Missing)61.7%
 
ValueCountFrequency (%) 
179.110.3%
 
179.910.3%
 
180.710.3%
 
184.510.3%
 
187.710.3%
 
ValueCountFrequency (%) 
442.410.3%
 
417.710.3%
 
414.810.3%
 
409.310.3%
 
408.210.3%
 

production_total_production_ipu_thsdtonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct276
Distinct (%)86.8%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean222.3051258
Minimum153.7
Maximum298.1
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:26.021029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum153.7
5-th percentile163.025
Q1200.6
median219.95
Q3246.29
95-th percentile283.46
Maximum298.1
Range144.4
Interquartile range (IQR)45.69

Descriptive statistics

Standard deviation35.36624077
Coefficient of variation (CV)0.1590887328
Kurtosis-0.6121700279
Mean222.3051258
Median Absolute Deviation (MAD)21.95
Skewness-0.01334310384
Sum70693.03
Variance1250.770986
MonotocityNot monotonic
2022-04-08T10:20:26.205249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
236.730.8%
 
206.530.8%
 
210.230.8%
 
291.520.6%
 
164.620.6%
 
247.420.6%
 
160.720.6%
 
238.820.6%
 
162.620.6%
 
200.620.6%
 
Other values (266)29581.9%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
153.710.3%
 
154.810.3%
 
156.510.3%
 
156.610.3%
 
156.810.3%
 
ValueCountFrequency (%) 
298.110.3%
 
295.510.3%
 
29510.3%
 
294.110.3%
 
293.410.3%
 
Distinct293
Distinct (%)92.1%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean268.2516981
Minimum204.9
Maximum380.8
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:26.432060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum204.9
5-th percentile223.085
Q1242.55
median258.75
Q3289.55
95-th percentile341.045
Maximum380.8
Range175.9
Interquartile range (IQR)47

Descriptive statistics

Standard deviation35.26600986
Coefficient of variation (CV)0.1314661197
Kurtosis0.3492882818
Mean268.2516981
Median Absolute Deviation (MAD)22.55
Skewness0.876116963
Sum85304.04
Variance1243.691451
MonotocityNot monotonic
2022-04-08T10:20:26.610585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
250.930.8%
 
294.420.6%
 
246.820.6%
 
232.820.6%
 
243.620.6%
 
245.520.6%
 
286.420.6%
 
25220.6%
 
283.520.6%
 
236.220.6%
 
Other values (283)29782.5%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
204.910.3%
 
21310.3%
 
215.810.3%
 
21610.3%
 
216.210.3%
 
ValueCountFrequency (%) 
380.810.3%
 
379.110.3%
 
373.310.3%
 
372.710.3%
 
363.410.3%
 

production_layers_compounds_thsdtonnes
Real number (ℝ≥0)

MISSING

Distinct232
Distinct (%)73.0%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean97.51874214
Minimum81.9
Maximum127.9
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:26.903799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum81.9
5-th percentile85.555
Q190.225
median96.4
Q3102.3
95-th percentile116.245
Maximum127.9
Range46
Interquartile range (IQR)12.075

Descriptive statistics

Standard deviation9.350092005
Coefficient of variation (CV)0.09587994881
Kurtosis0.561038973
Mean97.51874214
Median Absolute Deviation (MAD)6.05
Skewness0.8536777729
Sum31010.96
Variance87.42422049
MonotocityNot monotonic
2022-04-08T10:20:27.157356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10161.7%
 
90.541.1%
 
100.941.1%
 
95.641.1%
 
86.530.8%
 
102.830.8%
 
102.130.8%
 
85.630.8%
 
94.330.8%
 
100.730.8%
 
Other values (222)28278.3%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
81.910.3%
 
82.110.3%
 
82.910.3%
 
83.510.3%
 
83.710.3%
 
ValueCountFrequency (%) 
127.910.3%
 
12610.3%
 
125.710.3%
 
125.110.3%
 
122.710.3%
 

production_total_poultry_feed_thsdtonnes
Real number (ℝ≥0)

MISSING

Distinct289
Distinct (%)90.9%
Missing42
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean489.3670755
Minimum407
Maximum642.1
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2022-04-08T10:20:27.352091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum407
5-th percentile426.365
Q1452
median481.45
Q3520.45
95-th percentile575.15
Maximum642.1
Range235.1
Interquartile range (IQR)68.45

Descriptive statistics

Standard deviation47.64176269
Coefficient of variation (CV)0.0973538374
Kurtosis0.0861359754
Mean489.3670755
Median Absolute Deviation (MAD)34.35
Skewness0.7045914955
Sum155618.73
Variance2269.737553
MonotocityNot monotonic
2022-04-08T10:20:27.542892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
458.630.8%
 
444.930.8%
 
45230.8%
 
464.430.8%
 
44420.6%
 
525.920.6%
 
483.420.6%
 
539.720.6%
 
44820.6%
 
464.320.6%
 
Other values (279)29481.7%
 
(Missing)4211.7%
 
ValueCountFrequency (%) 
40710.3%
 
408.510.3%
 
408.910.3%
 
409.410.3%
 
410.610.3%
 
ValueCountFrequency (%) 
642.110.3%
 
621.310.3%
 
619.310.3%
 
616.710.3%
 
611.910.3%
 

Interactions

2022-04-08T10:19:59.789854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:19:59.935508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.140920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.301487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.454376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.666813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.829513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:00.992120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:01.186600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:01.361131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:01.522660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:01.710160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:01.871766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:02.040276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:02.207867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:02.375080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:02.601475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:02.807336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:03.004807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:03.229208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:03.455604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:03.739922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:03.987080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:04.192530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:04.452880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:04.678235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:04.896352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.109774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.292332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.459953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.664402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.828354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:05.994919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:06.168407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:06.335997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:06.508538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:06.717941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:06.938995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:07.121468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:07.305975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:07.487491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:07.704913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:07.904683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:08.121107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:08.286700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:08.461234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:08.703548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:08.873130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.045705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.224191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.387752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.562891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.794312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:09.973747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:10.158253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:10.319830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:10.483387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:10.799539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:10.992366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:11.189840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:11.443161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:11.680526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:11.862493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:12.072464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:12.325856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:12.513289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:12.784560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:12.984401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:13.267646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:13.450156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:13.689518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:13.866130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:14.086294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:14.268845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:14.434402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:14.629844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:15.015826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:15.226316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:15.519533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:16.072993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:16.642470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:16.887815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.034580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.237810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.419640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.642045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.833749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:17.999878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:18.238205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:18.402800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:18.567322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:18.776762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:18.931390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:19.122836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:19.287398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:19.472199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:19.680598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:19.871480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:20.052956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:20.209539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-08T10:20:27.789192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-08T10:20:28.131924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-08T10:20:28.504617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-08T10:20:28.905586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-08T10:20:20.507778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:21.013119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:21.438001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T10:20:21.916682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

itemyearmonthproduction_broiler_chicken_compounds_ipu_thsdtonnesproduction_broiler_chicken_compounds_rtl_thsdtonnesproduction_layers_compounds_ipu_thsdtonnesproduction_layers_compounds_rtl_thsdtonnesproduction_total_poultry_feed_rtl_thsdtonnesproduction_total_production_ipu_thsdtonnesproduction_broiler_chicken_compounds_thsdtonnesproduction_layers_compounds_thsdtonnesproduction_total_poultry_feed_thsdtonnes
01992.0AugNaN68.7NaN61.9217.3NaNNaNNaNNaN
11992.0DecNaN71.9NaN72.1223.4NaNNaNNaNNaN
21992.0JulNaN84.1NaN69.6257.1NaNNaNNaNNaN
31992.0NovNaN69.2NaN62.0219.2NaNNaNNaNNaN
41992.0OctNaN79.3NaN69.1245.0NaNNaNNaNNaN
51992.0SepNaN75.3NaN63.3227.9NaNNaNNaNNaN
61993.0AprNaN79.5NaN68.5232.6NaNNaNNaNNaN
71993.0AugNaN76.0NaN57.4219.0NaNNaNNaNNaN
81993.0DecNaN82.1NaN72.0235.3NaNNaNNaNNaN
91993.0FebNaN72.9NaN58.8200.1NaNNaNNaNNaN

Last rows

itemyearmonthproduction_broiler_chicken_compounds_ipu_thsdtonnesproduction_broiler_chicken_compounds_rtl_thsdtonnesproduction_layers_compounds_ipu_thsdtonnesproduction_layers_compounds_rtl_thsdtonnesproduction_total_poultry_feed_rtl_thsdtonnesproduction_total_production_ipu_thsdtonnesproduction_broiler_chicken_compounds_thsdtonnesproduction_layers_compounds_thsdtonnesproduction_total_poultry_feed_thsdtonnes
3502021.0May133.6185.78.893.0353.7173.5319.3101.8527.2
3512021.0Nov122.9163.38.592.5330.3166.7286.2101.0497.0
3522021.0Oct122.0160.28.390.0325.0165.0282.298.3490.0
3532021.0Sep148.5208.110.1115.6417.7199.0356.6125.7616.7
3542022.0AprNaNNaNNaNNaNNaNNaNNaNNaNNaN
3552022.0FebNaNNaNNaNNaNNaNNaNNaNNaNNaN
3562022.0JanNaNNaNNaNNaNNaNNaNNaNNaNNaN
3572022.0JunNaNNaNNaNNaNNaNNaNNaNNaNNaN
3582022.0MarNaNNaNNaNNaNNaNNaNNaNNaNNaN
3592022.0MayNaNNaNNaNNaNNaNNaNNaNNaNNaN